Question classification and feature mapping in a deep question answering system

ABSTRACT

System, method, and computer program product to identify relevant features in a deep question answering system, by classifying a first case received by the deep question answering system, and, while training the deep question answering system to answer the first case, identifying a first feature in the first case, computing a first feature score for the first feature, the first feature score indicating a relevance of the first feature in generating a correct response to the first case, and, identifying the first feature as relevant in answering the classified first case upon determining that the first feature score exceeds a relevance threshold.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to co-pending U.S. patent application Ser.No. ______, Attorney Docket Number ROC920120186US2 and co-pending U.S.patent application Ser. No. ______, Attorney Docket NumberROC920120186US3, which were all filed on the same day as the presentapplication. Each of the aforementioned related patent applications isherein incorporated by reference in its entirety.

BACKGROUND

Embodiments disclosed herein relate to the field of computer software.More specifically, embodiments disclosed herein relate to computersoftware which implements question classification and feature mapping ina deep question answering system.

SUMMARY

Embodiments disclosed herein provide a system, method, and computerprogram product to identify relevant features in a deep questionanswering system, by classifying a first case received by the deepquestion answering system, and, while training the deep questionanswering system to answer the first case, identifying a first featurein the first case, computing a first feature score for the firstfeature, the first feature score indicating a relevance of the firstfeature in generating a correct response to the first case, and,identifying the first feature as relevant in answering the classifiedfirst case upon determining that the first feature score exceeds arelevance threshold.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

So that the manner in which the above recited aspects are attained andcan be understood in detail, a more particular description ofembodiments of the disclosure, briefly summarized above, may be had byreference to the appended drawings.

It is to be noted, however, that the appended drawings illustrate onlytypical embodiments of this disclosure and are therefore not to beconsidered limiting of its scope, for the disclosure may admit to otherequally effective embodiments.

FIG. 1 is a block diagram illustrating a system for questionclassification and feature mapping in deep question answering systems,according to one embodiment disclosed herein.

FIG. 2 is a flow chart illustrating a method for generating a responseto a case by a deep question answering system, according to oneembodiment disclosed herein.

FIG. 3 is a flow chart illustrating a method for training a deepquestion answering system to classify questions and map features,according to one embodiment disclosed herein.

FIG. 4 is a flow chart illustrating a method for monitoring evidence todetect changes in the evidence, according to one embodiment disclosedherein.

FIG. 5 is a flow chart illustrating a method for processing a questionusing partial and parallel pipeline execution, according to oneembodiment disclosed herein.

FIG. 6 is a block diagram illustrating components of a deep questionanswering system, according to one embodiment disclosed herein.

DETAILED DESCRIPTION

Embodiments disclosed herein train a deep question answering system(deep QA system) to classify questions, identify features (orannotators) which are the most relevant in generating an answer to thoseclasses of questions, and store a mapping identifying the relationship.The questions may be classified based on type, information related tothe person or entity asking the question, and other contextualinformation. Once highly relevant features for each class of questionare identified, the embodiments disclosed herein may look for thesefeatures when answering another question of the same or similar class.If the feature is not part of a candidate answer for a similar question,processing of the candidate answer may be skipped in order to improvethe amount of time and processing required to generate a response tothat case. Additionally, embodiments disclosed herein monitor evidencelinked to the highly relevant features for changes that maysignificantly impact a confidence in previously generated answers basedon the unchanged evidence. Upon detecting a change in the evidence, thedeep QA system may reprocess old questions or generate new questions totest whether a change in evidence results in a change in the correctanswer. The deep QA system may also inform users who previously askedquestions if the evidence change affected the correct answer to theirquestions.

Generally, embodiments disclosed herein address the need for frequentquestion and answer analysis in a deep QA system. This need ishighlighted in financial services, social networking, and marketingcontexts, where near real-time information and answers are critical. Insuch environments, similar questions may be asked by multiple usersevery second, and any processing that may be eliminated may improve theperformance of the deep QA system. A feature, as used herein, may bedefined as a concept used to identify evidence which is used to generatea response to a case presented to the deep question answering system. Afeature may be a calculated or generated score or characteristicproduced in a specific manner. For example, a feature may measure theexistence of some characteristic, or it may try to evaluate the entireaccuracy of a given candidate answer for the current question.Individual features may be used in conjunction with machine learning todetermine the final score for a given candidate answer. A case (alsoreferred to as a question), which may comprise multiple questions, maybe a query presented to the deep QA system.

A deep QA system may process cases through a single analysis “pipeline.”A pipeline may represent the execution of various analysis programs, orengines, on both the question text and candidate answers (i.e., textpassages extracted from documents in a corpus) in order to deduce aprobable correct answer. A typical pipeline may begin with questionanalysis, which analyzes and annotates each question presented in thecase to identify key attributes upon which a search may be conducted.The next step of the pipeline may include a primary search, whichinvolves searching for documents in the corpus using the key attributesfrom the question analysis phase. The deep QA system may then generatecandidate answers, which may involve identifying key matching passagesfrom the search results with passages in the candidate answers. The deepQA system may then retrieve supporting evidence for the candidateanswers. Finally, the deep QA system may complete the pipeline byscoring the various candidate answers, from which a correct answer maybe selected.

Unique pipelines may be created for each domain or problem space (e.g. adifferent pipeline is used for supporting cancer treatments, insuranceclaims, diagnoses, and general knowledge, etc.). In fact, analysisengines themselves may be unique to a particular domain (e.g.,identification of a tumor stage or size, identification of drugs,potential drug interactions, etc.). Question and answer analysis withina pipeline may also include complex natural language processingalgorithms, used, for example, to identify deep semantic relationshipswithin the text. The scoring phase of a deep QA system, such as IBM'sWatson, may call various scoring algorithms to help deduce a correctanswer (or response) to a case. A scoring algorithm may generate one ormore feature scores to indicate how confident it is in its answer. Thedeep QA system may also use a training phase to learn which features, orcombinations of features, are best at predicting the right answers fordifferent types of questions. Once the deep QA system has been properlytrained, subsequent questions flowing through the pipeline may use themachine-learned model for finding the most likely correct answer.

In the following, reference is made to embodiments of the disclosure.However, it should be understood that the disclosure is not limited tospecific described embodiments. Instead, any combination of thefollowing features and elements, whether related to differentembodiments or not, is contemplated to implement and practice thedisclosure. Furthermore, although embodiments of the disclosure mayachieve advantages over other possible solutions and/or over the priorart, whether or not a particular advantage is achieved by a givenembodiment is not limiting of the disclosure. Thus, the followingaspects, features, embodiments and advantages are merely illustrativeand are not considered elements or limitations of the appended claimsexcept where explicitly recited in a claim(s). Likewise, reference to“the invention” shall not be construed as a generalization of anyinventive subject matter disclosed herein and shall not be considered tobe an element or limitation of the appended claims except whereexplicitly recited in a claim(s).

As will be appreciated by one skilled in the art, aspects of the presentdisclosure may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present disclosure may take theform of an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present disclosure may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present disclosure are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

Embodiments of the disclosure may be provided to end users through acloud computing infrastructure. Cloud computing generally refers to theprovision of scalable computing resources as a service over a network.More formally, cloud computing may be defined as a computing capabilitythat provides an abstraction between the computing resource and itsunderlying technical architecture (e.g., servers, storage, networks),enabling convenient, on-demand network access to a shared pool ofconfigurable computing resources that can be rapidly provisioned andreleased with minimal management effort or service provider interaction.Thus, cloud computing allows a user to access virtual computingresources (e.g., storage, data, applications, and even completevirtualized computing systems) in “the cloud,” without regard for theunderlying physical systems (or locations of those systems) used toprovide the computing resources.

Typically, cloud computing resources are provided to a user on apay-per-use basis, where users are charged only for the computingresources actually used (e.g. an amount of storage space consumed by auser or a number of virtualized systems instantiated by the user). Auser can access any of the resources that reside in the cloud at anytime, and from anywhere across the Internet. In context of the presentdisclosure, a user may access a deep question answering system orrelated data available in the cloud. For example, the deep questionanswering system could execute on a computing system in the cloud andprovide question classification and feature mapping. In such a case, thedeep question answering system could classify questions, map featuresand store the resultant data sets at a storage location in the cloud.Doing so allows a user to access this information from any computingsystem attached to a network connected to the cloud (e.g., theInternet).

FIG. 1 is a block diagram illustrating a system 100 for questionclassification and feature mapping in deep question answering systems,according to one embodiment disclosed herein. The networked system 100includes a computer 102. The computer 102 may also be connected to othercomputers via a network 130. In general, the network 130 may be atelecommunications network and/or a wide area network (WAN). In aparticular embodiment, the network 130 is the Internet.

The computer 102 generally includes a processor 104 connected via a bus120 to a memory 106, a network interface device 118, a storage 108, aninput device 122, and an output device 124. The computer 102 isgenerally under the control of an operating system (not shown). Examplesof operating systems include the UNIX operating system, versions of theMicrosoft Windows operating system, and distributions of the Linuxoperating system. (UNIX is a registered trademark of The Open Group inthe United States and other countries. Microsoft and Windows aretrademarks of Microsoft Corporation in the United States, othercountries, or both. Linux is a registered trademark of Linus Torvalds inthe United States, other countries, or both.) More generally, anyoperating system supporting the functions disclosed herein may be used.The processor 104 is included to be representative of a single CPU,multiple CPUs, a single CPU having multiple processing cores, and thelike. Similarly, the memory 106 may be a random access memory. While thememory 106 is shown as a single identity, it should be understood thatthe memory 106 may comprise a plurality of modules, and that the memory106 may exist at multiple levels, from high speed registers and cachesto lower speed but larger DRAM chips. The network interface device 118may be any type of network communications device allowing the computer102 to communicate with other computers via the network 130.

The storage 108 may be a persistent storage device. Although the storage108 is shown as a single unit, the storage 108 may be a combination offixed and/or removable storage devices, such as fixed disc drives, solidstate drives, floppy disc drives, tape drives, removable memory cards oroptical storage. The memory 106 and the storage 108 may be part of onevirtual address space spanning multiple primary and secondary storagedevices.

As shown, the memory 106 contains the QA application 112, which is anapplication generally configured to operate a deep question answering(QA) system. One example of a deep question answering system is Watson,by the IBM Corporation of Armonk, N.Y. A user may submit a case (alsoreferred to as a question) to the QA application 112, which will thenprovide an answer to the case based on an analysis of a corpus ofinformation. The QA application 112 may execute a pipeline to generate aresponse to the case, which is returned to the user. The QA application112 may further be configured to classify questions, identify featureshighly relevant to generating a correct response to the questions, andstore the resulting relationships for later use. The QA application 112may further be configured to perform partial or parallel pipelineexecution. For example, if a class of question has been identified ashaving a feature highly predictive of a correct answer, and a candidateanswer for a question received by the QA application 112 does notcontain the feature, the QA application 112 may not process thatcandidate answer to improve performance and conserve resources. Finally,the QA application 112 may monitor evidence underlying relevant featuresfor changes. If a change is detected, and the QA application 112believes that the change may impact a generated answer, the QAapplication 112 may reprocess the question to ensure that a correctresponse is provided. Additionally, the QA application 112 may generatenew questions, based on the changed evidence, to determine whether theanswer changed along with the evidence. Upon detecting the change inevidence, the QA application 112 may inform users that responses totheir previously submitted cases may have changed based on the change inevidence.

As shown, storage 108 contains the ontology 110, corpus 114, featurestore 115, ML models 116, and evidence classification 117. The ontology110 provides a structural framework for organizing information. Anontology formally represents knowledge as a set of concepts within adomain, and the relationships between those concepts. The corpus 114 isa body of information used by the QA application 112 to generate answersto cases. For example, the corpus 114 may contain scholarly articles,dictionary definitions, encyclopedia references, and the like. Featurestore 115 stores a mapping between question context and features whichare highly predictive in generating a response to the question, suchthat the features may be identified when processing additional questionscontaining the same context. If the features are not present incandidate answers for the question, processing of those candidateanswers by the QA application 112 may be skipped to improve performanceand efficiency. Machine learning (ML) models 116 are models created bythe QA application 112 during the training phase, which are used duringa runtime pipeline to score and rank candidate answers to cases based onfeatures previously generated for each answer. Evidence classification117 stores relationships between evidence from the corpus 114, thequestion context, and the predictive features. Based on these storedrelationships, the QA application may monitor the underlying evidencefor a change. If a change is detected, and the QA application 112determines that the change undermines a degree of confidence in responsegenerated using the evidence, the QA application 112 may reprocess thequestions, or alert users to the change. Although depicted as adatabase, ontology 110, corpus 114, feature store 115, ML models 116,and evidence classification 117 may take any form sufficient to storedata, including text files, xml data files, and the like. In oneembodiment, the ontology 110 is part of the corpus 114. Althoughdepicted as residing on the same computer, any combination of the QAapplication 112, the ontology 110, corpus 114, feature store 115, MLmodels 116, and evidence classification 117 may reside on the same ordifferent computers.

The input device 122 may be any device for providing input to thecomputer 102. For example, a keyboard and/or a mouse may be used. Theoutput device 124 may be any device for providing output to a user ofthe computer 102. For example, the output device 124 may be anyconventional display screen or set of speakers. Although shownseparately from the input device 122, the output device 124 and inputdevice 122 may be combined. For example, a display screen with anintegrated touch-screen may be used.

FIG. 2 is a flow chart illustrating a method 200 for generating aresponse to a case by a deep question answering system, according to oneembodiment disclosed herein. The method 200 modifies the traditionalexecution pipeline of the QA application 112, such that the pipelineincludes question classification and feature mapping, partial orparallel pipeline execution, and evidence monitoring to detect changeswhich may impact the correctness of generated responses. In oneembodiment, the QA application 112 performs the steps of the method 200.At step 210, the QA application 112 is trained to classify questions andmap features to the questions based on a sample input case. During thetraining process, the QA application 112 identifies which combination offeatures was “essential,” or strongly indicative for answering thequestions in the case. Stated differently, the QA application 112 mayidentify which feature, or combination of features, has the greatestweight in generating a correct answer based on the context of thequestion. When a subsequent question is asked, the QA application 112may determine how similar the question is to the previously processedquestions. If a computed similarity measure exceeds a predefinedsimilarity threshold, the QA application 112 may only use the essentialfeatures in generating a response to the question. For example, if afirst case includes a question on whether it is a good time to buy ahouse, the QA application 112 may identify a training question relatedto whether people should rent or buy homes. The QA application 112 maycompute a similarity score of the two cases by analyzing the concepts,features, and related data. If the cases share sufficient commonalities,the QA application 112 may compute a similarity score that exceeds thesimilarity threshold, such that when the first case is processed, thefeatures of the training case are utilized. The step 210 is discussed ingreater detail with reference to FIG. 3.

At step 220, the QA application 112 monitors evidence to detect changesin the evidence which may impact the confidence in a generated response.Although depicted as a single step, the QA application 112 maycontinuously monitor the evidence in the corpus 114, even in the absenceof a case being processed, such that the confidence of previouslygenerated answers is maintained. The step 220 is discussed in greaterdetail with reference to FIG. 4. At step 230, the QA application 112receives an input case, which may comprise multiple questions, from auser. At step 240, the QA application 112 may process the question usingpartial and parallel pipeline execution. In one embodiment, the fullpipeline may be executed at step 240. Generally, partial pipelineexecution may be implemented to limit the amount of processing completedby the QA application 112 to improve system performance. The QAapplication 112 may skip the processing of candidate answers notcontaining the features identified at step 210 in order to reduce theamount of processing, and therefore time, needed to return an answer. Atstep 250, the QA application 112 returns a response to the case.

FIG. 3 is a flow chart illustrating a method 300 corresponding to step210 for training a deep question answering system to classify questionsand map features, according to one embodiment disclosed herein. In oneembodiment, the QA application 112 performs the steps of the method 300.Generally, during the training phase, the QA application 112 determineshow a question of a case should be classified, or categorized, based onseveral factors, including, but not limited to, the question type,information (or metadata) related to the person or entity asking thequestion, and other contextual information. The training phase maycomprise an entire pipeline. During the answer scoring and evidencescoring phase of the training pipeline, the QA application 112 mayidentify the most predictive features, or annotators, for the particularquestion type. The question context (including all relevant informationused to classify the question), as well as any associated highlypredictive features, may be written to the feature classification 117for later use.

At step 310, the QA application 112 receives the training case andmetadata related to the user asking the question. The metadata of theuser may include the user's role, affiliation, expertise, preferences,or any other attribute of the user, or an entity the user represents.When subsequent cases are received by the QA application 112, the usermetadata of that user may be analyzed when computing a similarity scoreto previously asked (or training) questions, and stored in the featurestore 115. At step 320, the QA application 112 begins executing a loopcontaining steps 330-395 for each question in the training case. At step330, the QA application 112 identifies the question type, or context.Any suitable /method may be used to identify the question context,including natural language processing to extract normalized terms andconcepts from the question. At step 340, the QA application 112generates a response to the case, and identifies the features used ingenerating the response. For example, if the question relates to whetherthe exchange rate for the Japanese Yen and the U.S. Dollar is favorableon a particular day, a plurality of variables, or features, may beconsidered. The features may include whether the currencies, areovervalued, the price of gold, and current inflation rates. The QAapplication, in generating the response indicating whether the exchangerate is favorable, may also store information related to which feature,or combination of features, was most relevant in generating the responseto the question.

At step 350, the QA application 112 begins executing a loop includingsteps 360-390 for scoring each feature identified at step 340. At step360, the QA application 112 computes a feature score for the feature.The feature score may be computed by any suitable measure, including theability of the feature to lead to a correct answer for the question orcase. A feature score may be produced using an algorithm of rangingcomplexity to produce a representative score for a specific candidateanswer. The algorithm may, for example, compare the number of similarwords between the candidate answer and the question, or it may parse themeaning of the question to match terms, context, negation, plurality,conjunctions, and chronology, for example. The feature score itself maybe a number in a permissible range of feature scores. At step 370, theQA application 112 determines whether the feature score exceeds apredefined feature threshold. The feature threshold may be a defaultthreshold, or a threshold specified by the user. If the computed featurescore exceeds the feature threshold, the feature is added to the featureclassification store 117 at step 380. The feature, user metadata, andquestion context (including all relevant information used to classifythe question), may be written to the feature classification store 117 asrelated entities. If the computed feature score does not exceed thefeature threshold, the QA application 112 proceeds to step 390. At step390, the QA application 112 determines whether more features remain tobe scored. If more features remain, the QA application 112 returns tostep 350. Otherwise, the QA application 112 proceeds to step 395. Atstep 395, the QA application 112 determines whether more questions ofthe training case remain. If more questions remain, the QA application112 returns to step 320. Otherwise, the training phase is complete andthe method 300 ends.

FIG. 4 is a flow chart illustrating a method 400 corresponding to step220 for monitoring evidence to detect changes in the evidence, accordingto one embodiment disclosed herein. Generally, the steps of the method400 are executed to detect significant changes in key supportingevidence, providing an automated way to trigger partial or parallelpipeline execution. To accomplish this, critical evidence needed toanswer a given question, or type of question, which has been run througha full pipeline is identified. For example, the QA application 112 mayreceive a case containing a question which asks, “Is the exchange ratefor the Japanese Yen and U.S. Dollar favorable today?” In order toanswer the question, the QA application 112 may need to consider anumber of features (or variables) related to the Tokyo Stock Exchange,including whether the dollar and yen are overvalued, whether gold pricesare overvalued, and whether the U.S. and Japanese inflation rates areabove normal. All of these features may rely on underlying data(supporting evidence), which changes very frequently. Through machinelearning, the QA application 112 may determine that when the U.S.inflation rate is greater than some value, it is never a good idea toexchange yen for dollars, regardless of other features. This isconsidered a “strong indicator.” When a strong indicator is identified,its impact on the final answer should be much greater than otherfeatures. However, this may not imply that a strong indicator is thesole input to determining an answer, but it may help implicate answersthat are completely inacceptable given the current evidence. Thefeatures, and the variables they may represent, may be more complex thansimple reference values. For example, the variable considering whetherthe U.S. dollar is overvalued may take into account expert insights andopinions contained within natural language documents in addition to thecurrent value of the U.S. dollar. Through the use of the method 400, theQA application 112 may utilize the correlation between the feature andthe underlying data to know that significant changes in the data willaffect the feature score. In one embodiment, the QA application 112implements a confidence score which indicates the overall confidencethat a change in the evidence will result in a change in the answer.

The QA application 112 may monitor both missing (identified, but notexistent) and existing evidence. The QA application 112 may note when afeature has identified highly relevant evidence which does not exist, orwhere it does not have sufficient evidence to produce a meaningfulconfidence score. However, when the QA application 112 detects that thedata has changed in meaningful ways, such as through the newly foundexistence of specific missing evidence, or a significant change inexisting evidence, the QA application 112 may execute another partialpipeline. If the partial pipeline indicates a high likelihood for achange in the answer, then the QA application 112 may execute a fullpipeline, and may notify users who have asked similar questions in thepast.

Additionally, the QA application 112 may generate a subset of the corpus114 which contains just those pieces of evidence, such as documents,which are both relevant and have a significant impact to a givenquestion. The QA application 112 may consider a number of relevancyfiltering methods, including the ability to select only documents whichcontain a sufficient number of concepts and attributes with are alsoexpressed within the case or question. Subsetting the corpus to onlythose documents which are applicable to a given question reduces thenumber of documents that have the potential to trigger full or partialreprocessing of the question.

At step 410, the QA application 112 identifies evidence linked torelevant features. In one embodiment, the QA application 112 identifiesthe evidence based on a comparison to the entries in evidenceclassification 117. As previously stated, the evidence classification117 may store relationships between the relevant features, the questioncontext, and evidence types. Therefore, the QA application 112 mayidentify evidence and compare its type to that in the evidenceclassification 117 to determine whether it comprises evidence which islinked to relevant features. The degree of impact a supporting evidencearticle has on a question may be determined by the features influencedby the article and the weight assigned to those features by the machinelearning model in use for the specific use case. One example of thisoptimization may be cases where there are no essential supportingevidence features detected through machine learning, e.g., the weightingassigned to supporting evidence features is very low for a given classof question. Thus, any change to the overall supporting evidence corpusmay be ignored without significant impact on answer confidence scoresfor that type of question.

At step 420, the QA application 112 monitors the evidence. As statedabove, the QA application 112 may monitor the entire corpus 114, or asubset of evidence related to a particular question class. At step 430,the QA application 112 determines whether a change in linked evidencehas occurred. If no change has been detected, the QA application 112returns to step 420. The change may be detected, in the case of missingevidence, by the presence of evidence. For existing evidence, the changemay be detected by monitoring a content of the evidence. Generally, anysuitable method may be used to monitor the evidence. If a change inevidence has been detected, the QA application 112 proceeds to step 440.Upon detecting a change in the evidence, the QA application 112 mayreprocess a previously submitted question, or generate a new question,to determine whether the confidence score for the question has changedsuch that the answer has changed as well. At step 440, the QAapplication 112, in generating a new question, retrieves the applicablequestion context data linked to the relevant features. By obtaining thecontext, the QA application 112 at step 450 may formulate a newquestion. At step 460, the QA application 112 may run a partial pipelineon the new question (or, the previously submitted question), and computea corresponding confidence score. The confidence score may be based onthe relevance score of the feature, a degree of change in the evidence,or any other suitable method. The confidence score may be any valuebased on a scale suitable to indicate a range of confidence scores. Atstep 470, the QA application 112 determines whether the confidence scoreexceeds the confidence threshold. The confidence threshold may be adefault threshold, or a user-defined threshold. If the confidence scoredoes not exceed the confidence threshold, the QA application 112 returnsto step 420. If the confidence score exceeds the confidence threshold,then the QA application 112 may determine, to a certain degree ofprobability, that the answer to the class of question may have changedalong with the evidence. Therefore, if the confidence score exceeds theconfidence threshold, the QA application 112 proceeds to step 470, wherethe QA application 112 executes a full pipeline, and optionally maynotify interested parties who have previously submitted similarquestions.

FIG. 5 is a flow chart illustrating a method 500 corresponding to step240 for processing a question using partial and parallel pipelineexecution, according to one embodiment disclosed herein. The steps ofthe method 500 may be executed to improve efficiency of responsesgenerated to frequently asked questions. The QA application 112 may usethe context-mapped features identified in step 210 to run partialpipelines to improve efficiency. A partial pipeline is one in which notall phases need to be run in order for the QA application 112 to bereasonably certain that a previously generated response has not changed.To make this possible, the QA application 112 keeps track of thedependencies for each essential feature scorer. When a partial pipelineis executed, only the minimum number of steps required to fire eachessential feature is executed, enabling the QA application 112 to “shortcircuit” the full pipeline. Thus, the QA application 112 will executeonly those annotators associated with the highly predictive features. Inone embodiment, the QA application 112 may keep track of all classes andexternal resources used by a feature-generating class during thetraining phase. Once these essential annotators are identified, theanswer scoring, supporting evidence retrieval, and evidence scoringcomponents of the pipeline may be executed for the candidate answershaving the relevant features. For example, a full pipeline may not beexecuted on a candidate answer not containing a feature whose featurescore exceeds the feature threshold. In such an event, the QAapplication 112 may skip the execution of several steps of the fullpipeline, including running natural language processing on the candidateanswer, determining whether the candidate answer contains a differenthighly relevant feature, retrieving supporting evidence for thecandidate answer, and scoring the supporting evidence for the candidateanswer.

At step 505, the QA application 112 may identify the context ofquestions in the case. At step 510, the QA application 112 beginsexecuting a loop including steps 515-560 for each question in the case.At step 515, the QA application 112 computes a similarity score for thequestion relative to the plurality of questions stored in the featurestore 115. The similarity score may be based on any number of factors,including a comparison of normalized concepts in the question and theconcepts stored in the feature store 115 related to the questionclass/type. At step 520, the QA application 112 determines whether thecomputed similarity score exceeds a similarity threshold. The similaritythreshold may be any range of values, and may be a system default oruser-defined threshold. If the similarity score exceeds the threshold,indicating that the questions are sufficiently similar, the QAapplication 112 proceeds to step 525. Otherwise, the QA application 112proceeds to step 560. At step 525, the QA application 112 identifieshighly relevant features from the feature store 115 related to the typeof case. Additionally, at step 525, the QA application 112 may retrievecandidate answers it has generated for the question.

At step 530, the QA application 112 executes a loop including steps535-555 for each candidate answer to the current question. At step 535,the QA application 112 determines whether the candidate answer containseach feature identified as highly relevant in the feature store 115 forthat type of question. If the candidate answer contains the feature,then further processing of that candidate answer may be required, andthe QA application 112 proceeds to step 555. Although not depicted, theQA application 112 may execute a full pipeline on the candidate answercontaining the feature. If the candidate answer does not contain thefeature, the QA application 112 proceeds to step 540, where the QAapplication 112 may generate a response to the question withoutprocessing the candidate answer, thereby saving system resources by notperforming unnecessary processing of the candidate answers notcontaining the highly relevant feature. At step 545, the QA application112 determines whether to implement parallel processing in times wheresystem resources are plenary. At step 545, the QA application 112determines whether a confidence threshold is exceeded by a confidencescore for the response generated by the partial pipeline, and whethersufficient resources exist. The confidence threshold may be related tothe confidence threshold discussed with reference to FIG. 4, in that itconveys a degree of confidence as to whether there would be a likelychange in the answer based on the execution of the partial pipeline. Ifsufficient resources exist and the confidence threshold has beenexceeded, the QA application 112 proceeds to step 550. Otherwise, the QAapplication 112 proceeds to step 555. At step 550, the QA application112 runs a full pipeline in parallel with the partial pipeline togenerate an additional response using the candidate answer. The QAapplication 112 may then compare the responses to determine which has agreater confidence score, and report its findings to the user asking thequestion, or may simply present the response having the greaterconfidence score. At step 555, the QA application 112 determines whethermore candidate answers remain to be analyzed. If more candidate answersremain, the QA application 112 returns to step 530. Otherwise, the QAapplication 112 proceeds to step 560. At step 560, the QA application112 determines whether more questions remain in the case. If morequestions remain, the QA application 112 returns to step 510. Otherwise,the method 500 ends.

FIG. 6 is a block diagram illustrating components of a deep questionanswering system, according to one embodiment disclosed herein. In oneembodiment, the deep question answering system is the QA application112. As shown, the QA application 112 contains a question classifier andfeature mapping component 601, a partial and parallel pipeline executioncomponent 602, and an intelligent evidence notification component 603.The question classifier and feature mapping component 601 may, duringthe training phase, classify questions based on type, and identify thefeatures which are most highly relevant in generating a correct responseto the questions in a training case. Once identified, the features maybe stored in a feature store, such as feature store 115. The partial andparallel pipeline execution component 602 may, when presented with asubsequent case, access the dependencies defined in the feature store115 to reduce processing of candidate answers that do not have thefeatures identified as highly relevant to answering a particular classof question. The intelligent evidence notification component 603 mayidentify relevant types of evidence and monitor evidence of this type todetermine whether it has changed. The evidence may be existent ornon-existent. Upon detecting a change, the intelligent evidencenotification component 603 may predict whether the change in evidencehas an impact on the confidence of a response generated using thechanged evidence. In such a scenario, the intelligent evidencenotification component 603 may reprocess questions to determine whetherthe answer has changed, and may further notify interested parties of theresults.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

While the foregoing is directed to embodiments of the presentdisclosure, other and further embodiments of the disclosure may bedevised without departing from the basic scope thereof, and the scopethereof is determined by the claims that follow.

What is claimed is:
 1. A computer program product to identify relevantfeatures in a deep question answering system, the computer programproduct comprising: a computer-readable storage medium havingcomputer-readable program code embodied therewith, the computer-readableprogram code comprising: computer-readable program code configured toclassify a first case received by the deep question answering system;and computer-readable program code configured to, while training thedeep question answering system to answer the first case:computer-readable program code configured to identify a first feature inthe first case; computer-readable program code configured to compute asimilarity score for the first case and the second case; andcomputer-readable program code configured to identify the first featureas relevant in generating a correct response to the second case upondetermining that the similarity score exceeds a specified similaritythreshold.
 2. The computer program product of claim 1, furthercomprising: computer-readable program code configured to, responsive toreceiving a second case by the deep question answering system: classifythe second case; compute a similarity score for the first case and thesecond case; and identify the first feature as relevant in generating acorrect response to the second case upon determining that the similarityscore exceeds a specified similarity threshold.
 3. The computer programproduct of claim 2, wherein the similarity score is based on at leastone of: (i) the classification of the first case and the classificationsecond case, and (ii) a context of the first case and a context of thesecond case.
 4. The computer program product of claim 2, wherein thefirst case and the second case are classified based on at least one of:(i) an attribute of a user presenting the respective case to the deepquestion answering system, (ii) a content of a question in therespective case, and (iii) a type of the question in the respectivecase.
 5. The computer program product of claim 4, wherein the attributeof the user is selected from the group comprising: (i) a role of theuser, (ii) an affiliation of the user, (iii) an expertise of the user,and (iv) a set of predefined preferences of the user.
 6. The computerprogram product of claim 1, further comprising: computer-readableprogram code configured to store a relationship between the firstfeature and the first case, wherein the relationship indicates that thefirst feature is relevant in answering the first case.
 7. The computerprogram product of claim 1, wherein the first feature score is based ona number of relevant items of supporting evidence retrieved by the deepquestion answering system using the first feature.
 8. The computerprogram product of claim 1, wherein the first case comprises at leastone question presented to the deep question answering system, whereinthe first feature comprises at least one of: (i) a type, (ii) a subjectmatter, (iii) a variable, and (iv) a context of the at least onequestion.
 9. A system, comprising: one or more computer processors; anda memory containing a program, which, when executed by the one or morecomputer processors, performs an operation to identify relevant featuresin a deep question answering system, the operation comprising:classifying a first case received by the deep question answering system;and while training the deep question answering system to answer thefirst case: identifying a first feature in the first case; computing afirst feature score for the first feature by operation of one or morecomputer processors, wherein the first feature score indicates arelevance of the first feature in generating a correct response to thefirst case; and identifying the first feature as relevant in answeringthe classified first case upon determining that the first feature scoreexceeds a relevance threshold.
 10. The system of claim 9, furthercomprising: responsive to receiving a second case by the deep questionanswering system: classifying the second case; computing a similarityscore for the first case and the second case; and identifying the firstfeature as relevant in generating a correct response to the second caseupon determining that the similarity score exceeds a specifiedsimilarity threshold.
 11. The system of claim 10, wherein the similarityscore is based on at least one of: (i) the classification of the firstcase and the classification second case, and (ii) a context of the firstcase and a context of the second case.
 12. The system of claim 10,wherein the first case and the second case are classified based on atleast one of: (i) an attribute of a user presenting the respective caseto the deep question answering system, (ii) a content of a question inthe respective case, and (iii) a type of the question in the respectivecase.
 13. The system of claim 12, wherein the attribute of the user isselected from the group comprising: (i) a role of the user, (ii) anaffiliation of the user, (iii) an expertise of the user, and (iv) a setof predefined preferences of the user.
 14. The system of claim 9,further comprising: storing a relationship between the first feature andthe first case, wherein the relationship indicates that the firstfeature is relevant in answering the first case.
 15. The system of claim9, wherein the first feature score is based on a number of relevantitems of supporting evidence retrieved by the deep question answeringsystem using the first feature.
 16. The system of claim 9, wherein thefirst case comprises at least one question presented to the deepquestion answering system, wherein the first feature comprises at leastone of: (i) a type, (ii) a subject matter, (iii) a variable, and (iv) acontext of the at least one question.